The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources

Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Ambulatory blood pressure monitoring or ABPM is a non-invasive method to determine the average blood pressure for at least 24 hours, not only when medical checkup. ABPM is often found in cardiac examinations and monitoring of catlab preoperative patients. This study aims to analyze the performance of the ABPM tool that can measure blood pressure continuously with a specified time interval connected to IoT so that can make it easier to get test results. The contribution of this research is a 24-hour monitoring system with delivery via IoT. The experiment was conducted 10 times with Prosim comparison at each point to assess the level of reading accuracy and effectiveness of IoT viewers. At 120/80 mmHg systole accuracy 98.42%, diastole 97.25%. While at 150/100 mmHg systole accuracy is 99.67%, Diastole is 98.1%. At 200/160 mmHg point Systole accuracy 98.35%, Diastole 98.25%. The SPSS test states that the reading data collection is acceptable and has an average commensurate with the test. The difference in viewer time on the TFT and IoT layers is 3.8 seconds and the test data value is 0% loss. The results from making this module, concluding by utilizing the sensor MPX5050 obtained sufficient accuracy, the use of ESP32 as a microcontroller processes the sensor readings which will be converted into systole-diastole values and displays on IoT so that it can slightly help analyze the patient's condition, and this module can read the simulator tool well at pressures of 120/80 mmHg, 150/100 mmHg, and 200/160 mmHg. The device showed good accuracy and reliability in measuring blood pressure at different levels compared to a vital signs simulator. The device can be used for 24-hour monitoring of hypertension patients and provide useful information for diagnosis and treatment

Using Machine Learning to address Data Accuracy and Information Integrity in Digital Health Delivery

Today, much of healthcare delivery is digital. In particular, there exists a plethora of mHealth solutions being developed. This in turn necessitates the need for accurate data and information integrity if superior mHealth is to ensue. Lack of data accuracy and information integrity can cause serious harm to patients and limit the benefits of mHealth technology. The described exploratory case study serves to investigate data accuracy and information integrity in mHealth, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information

Data accuracy considerations with mHealth

With the plethora of mHealth solutions developed being digital, this necessitates the need for accurate data and information integrity. Lack of data accuracy and information integrity in mHealth can cause serious harm to patients and limit the benefits of such promising technology. Thus, this exploratory study investigates data accuracy and information integrity in mHealth by examining a mobile health solution for diabetes, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons

Use of mHealth Technology for Patient-Reported Outcomes in Community-Dwelling Adults with Acquired Brain Injuries: A Scoping Review.

The purpose of our scoping review was to describe the current use of mHealth technology for long-term assessment of patient-reported outcomes in community-dwelling individuals with acquired brain injury (ABI). Following PRISMA guidelines, we conducted a scoping review of literature meeting these criteria: (1) civilians or military veterans, all ages; (2) self-reported or caregiver-reported outcomes assessed via mobile device in the community (not exclusively clinic/hospital); (3) published in English; (4) published in 2015-2019. We searched Ovid MEDLINE(R) \u3c 1946 to 16 August 2019, MEDLINE InProcess, EPub, Embase, and PsycINFO databases for articles. Thirteen manuscripts representing 12 distinct studies were organized by type of ABI [traumatic brain injury (TBI) and stroke] to extract outcomes, mHealth technology used, design, and inclusion of ecological momentary assessment (EMA). Outcomes included post-concussive, depressive, and affective symptoms, fatigue, daily activities, stroke risk factors, and cognitive exertion. Overall, collecting patient-reported outcomes via mHealth was feasible and acceptable in the chronic ABI population. Studies consistently showed advantage for using EMA despite variability in EMA timing/schedules. To ensure best clinical measurement, research on post-ABI outcomes should consider EMA designs (versus single time-point assessments) that provide the best timing schedules for their respective aims and outcomes and that leverage mHealth for data collection

Chapter 6 – Health Apps for Diagnostics and Therapy

Die Nutzung von mobilen Anwendungen auf Smartphones und Tablet-Computern im Gesundheitswesen und damit in der Medizin hat stark zugenommen. Evidenznachweise für medizinische Apps gibt es nur wenige. Ihre Nutzen in Diagnostik und Therapie besteht darin, an jedem Ort und zu jeder Zeit ortsunabhängige beliebige Informationen zu erfassen, aufzurufen, zu visualisieren und damit auch Entscheidungen zu treffen. Apps zur Diagnostik werden vorwiegend von medizinischem Fachpersonal genutzt, Apps zur Therapie hingegen von ärztlichem Personal wie von Patientinnen und Patienten gleichermaßen verwendet. Therapien können durch den Einsatz von Apps weiter optimiert werden, etwa durch gewonnene Mobilität oder durch geringere Kosten. Apps im Bereich Selbstmanagement, wie das Führen von Patiententagebüchern oder Apps, die an die Einnahme von Medikamenten erinnern, sind sinnvoll. Ähnlich sind Apps zu bewerten, die zu gesundheitsbewussten Verhalten anregen, etwa Trainingspläne für körperliche Übungen oder Ernährungstagebücher für die gesunde Ernährung. Tragbare Geräte wie Uhren, Brillen, Arm- und Körperbänder stellen eine unauffällige Lösung zur Überwachung von Vitalfunktion dar. Wearables können Nutzerinnen und Nutzern durch zusätzliche Funktionen somit Unterstützung bei der Erhaltung oder Verbesserung der Gesundheit bieten, sind jedoch teils mit hohen Kosten verbunden. Ärztinnen und Ärzten bieten die mobilen Anwendungen insbesondere bei erkrankten Patientinnen und Patienten Möglichkeiten, aktuelle Informationen über die Vitaldaten oder die derzeitige Situation der Betroffenen zu erhalten. Für Patientinnen und Patienten ist es einfach komfortabel, unabhängig von Zeit und Ort mit dem Arzt kommunizieren zu können. Die Betrachtung der Grenzen von Apps zeigt auf, dass eine Reihe von Barrieren und Herausforderungen vor dem nutzbringenden Einsatz dieser Tools stehen. Grundsätzlich bieten Apps die Chance zur Partizipation und Patientenbeteiligung und unterstützen in verschiedenen Phasen der Versorgungsprozesse im Gesundheitswesen.The use of mobile applications that are installed on smartphones and tablet computers has greatly increased in healthcare in general and thus also in medicine. However, scientific evidence with respect to the effectiveness of medical apps is still lacking. The possibility to diagnose and treat, in any place and at any time, to capture, access, visualize information and thus to come to decisions regardless of location, holds great promise, as do the rapid ways of transmitting medical data made possible by mobile technology. This is an essential aspect for modern aspects of telemedicine. Apps for diagnostics are primarily used by health care professionals, whereas apps with a therapeutic focus are used by both doctors and patients alike. Therapies can be further optimized by the use of apps, e.g. by providing mobility or through lower health care costs. Patient diaries or apps that instruct patients in the proper use of medication are helpful in patient self-management. So are apps that encourage health-conscious behavior, such as training plans for physical exercises or nutrition diaries for healthy eating. Portable devices such as watches, eyewear, and arm and body belts provide unobtrusive monitoring of vital function. Wearables equip users with additional functions that promote health. The downside lies in their high costs. Via mobile applications, doctors can receive and review up-to date information, e.g. vital signs or data about the patients' current situation. For patients, the added comfort of being able to communicate with their doctor regardless of time and place is a benefit. The evaluation of the limitations of apps shows that a number of barriers and challenges need to be overcome to take advantage of the beneficial aspects of these tools. In summary, apps offer the chance to involve patients actively in the management of their health and to support patients and doctors in various stages of the health care process

Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data

Mobile Personal Health System for Ambulatory Blood Pressure Monitoring

The ARVmobile v1.0 is a multiplatform mobile personal health monitor (PHM) application for ambulatory blood pressure (ABP) monitoring that has the potential to aid in the acquisition and analysis of detailed profile of ABP and heart rate (HR), improve the early detection and intervention of hypertension, and detect potential abnormal BP and HR levels for timely medical feedback. The PHM system consisted of ABP sensor to detect BP and HR signals and smartphone as receiver to collect the transmitted digital data and process them to provide immediate personalized information to the user. Android and Blackberry platforms were developed to detect and alert of potential abnormal values, offer friendly graphical user interface for elderly people, and provide feedback to professional healthcare providers via e-mail. ABP data were obtained from twenty-one healthy individuals (>51 years) to test the utility of the PHM application. The ARVmobile v1.0 was able to reliably receive and process the ABP readings from the volunteers. The preliminary results demonstrate that the ARVmobile 1.0 application could be used to perform a detailed profile of ABP and HR in an ordinary daily life environment, bedsides of estimating potential diagnostic thresholds of abnormal BP variability measured as average real variability